A master’s student did a big experiment looking at the effect of pre-adaptation and diversity of a focal species on community composition. The experiment was done in soil using a LacZ marked strain of Pseudomonas fluorescens.
Some Pseudomonas fluorescens were pre-adapted to the compost before being put into experimental microcosms with the resident community. Clones were then isolated from these pre-adaptation treatments by plating onto LacZ agar. They were then left for 6 weeks and at the end of the experiment all samples were squenced using Amplicon 16S sequencing. They got put through a sequencing pipeline using dada2 in R and the phylogenetic tree was constructed using Fasttree.
To tease apart pre-adaptation and diversity, there were a variety of treatments:
d <- tibble::tibble(treatment = c("individual_clone", "individual_clone", "4_related_clones", "4_related_clones", "4_unrelated_clones", "evolved_without_community", "evolved_with_community", "lacz_ancestor", "negative_control", "nmc_t0"),
evolved_with_community = c('yes', 'no', 'yes', 'no', 'NA', 'no', 'yes', 'no', 'NA', 'NA'),
reps = c(24, 24, 6, 6, 12, 6, 6, 6, 6, 1))
knitr::kable(d)
| treatment | evolved_with_community | reps |
|---|---|---|
| individual_clone | yes | 24 |
| individual_clone | no | 24 |
| 4_related_clones | yes | 6 |
| 4_related_clones | no | 6 |
| 4_unrelated_clones | NA | 12 |
| evolved_without_community | no | 6 |
| evolved_with_community | yes | 6 |
| lacz_ancestor | no | 6 |
| negative_control | NA | 6 |
| nmc_t0 | NA | 1 |
You can see that this is a somewhat unbalanced design, but as we are using permutational tests this should be ok (I think).
1. Looked at whether Pseudomonas fluorescens persists in the treatments
knitr::include_graphics(c('../sequencing/plots/fresh/prop_pseudomonas.png'))
2. Look at changes in community composition with preadaptation history and diversity of Pseudomonas fluorescens
Next we looked at how community composition may change with diversity and with preadaptation history (with and without the resident community). To do this we kept only 4_related_clones, individual_clone and evolved_with_community and evolved_without_community to give us a fully factorial design of treatments. We dropped factors that had no pre-adaptation stage (lacz_ancestor) and that were mixed between evolved with and without the community (4_unrelated_clones) and that had no additional Pseudomonas fluorescens added (negative_control).
betadisper() on a global model to get centroidsknitr::include_graphics(c('../sequencing/plots/fresh/effect_of_evol_history.png'))
3. Look specifically at diversity to see if diversity of clones changes the impact on the community
Because there seems to be an effect of diversity overall, higher diversity is closer to the natural microbial community and individual clone is closer to the lacz ancestor, we looked at changes in the position of centroids across levels of diversity (number of clones)
Had several different levels:
C_1 are the single clones, including the lacz ancestorsC_4 are the 4 clone treatments, both related and unrelatedC_24 are the 24 clone treatments, both evolved with and without the communityC_high are the negative control, the nmc after the treatmentd <- readRDS('../sequencing/data/output/mult_comp.rds')
knitr::kable(d)
| X1 | X2 | R2 | pval | pvalBon | pvalFDR | pvalHolm |
|---|---|---|---|---|---|---|
| C_1 | C_24 | 0.0693591 | 0.0029 | 0.0174 | 0.017 | 0.0174 |
| C_1 | C_4 | 0.0078370 | 0.6580 | 3.9480 | 0.658 | 0.6580 |
| C_1 | C_high | 0.0779211 | 0.0034 | 0.0204 | 0.010 | 0.0174 |
| C_24 | C_4 | 0.0950678 | 0.0146 | 0.0876 | 0.029 | 0.0584 |
| C_24 | C_high | 0.1329260 | 0.0336 | 0.2016 | 0.040 | 0.0672 |
| C_4 | C_high | 0.1262032 | 0.0169 | 0.1014 | 0.025 | 0.0584 |
Single clones are change community composition differently to the negative control and the 24 clone samples. There are no other significant differences in community composition with levels of diversity.
knitr::include_graphics(c('../sequencing/plots/fresh/PCoA_plot_diversity.png'))
Calculated loads of diversity metrics (i.e. Shannon, Simpsons, Observed OTU) and pielou’s evenness.
1. Look at how diversity and evenness change through treatments
knitr::include_graphics(c('../sequencing/plots/fresh/alpha_diversity.png'))
Alpha diversity seems a bit weird. Richness is largest in nmc_t0 (makes sense), but the negative control has the biggest reduction in observed OTUs (alongside evolved_without_community).
Not sure what to make of this…
1. Try and identify Pseudomonas fluorescens
knitr::include_graphics(c('../sequencing/plots/fresh/alex_pseudomonas_tree.png'))
knitr::include_graphics(c('../sequencing/plots/fresh/alex_pseudomonas_distance.png'))
2. How abundant are Pseudomonads and this sequence (SBW25)
knitr::include_graphics(c('../sequencing/plots/fresh/pseudomonas_abundance.png'))
knitr::include_graphics(c('../sequencing/plots/fresh/SBW25_prop.png'))
knitr::include_graphics(c('../sequencing/plots/fresh/prop_pseudomonas.png'))
knitr::include_graphics(c('../sequencing/plots/fresh/pseudomonas_diversity.png'))
knitr::include_graphics(c('../sequencing/plots/fresh/ind_clone_fitness.png'))
knitr::include_graphics(c('../sequencing/plots/predict_multiclones.png'))